Who Tells the Documentation Team? How AI Agents Disrupt Documentation Feedback Loops
TL;DR
This position paper uses systems thinking to trace how agent-mediated information seeking disrupts the balancing loops that currently contribute to documentation quality and how this disruption would create reinforcing loops that degrade documentation and code quality over time.
Abstract
Software documentation teams rely on developer activity to identify and correct problems in their content. As AI coding assistants reshape how developers seek information, their impact on software documentation feedback channels has gone largely unnoticed. When developers reduce reading documentation and stop posting questions, documentation teams lose the signals they need to find and fix problems. We use systems thinking to trace how agent-mediated information seeking disrupts the balancing loops that currently contribute to documentation quality and how this disruption would create reinforcing loops that degrade documentation and code quality over time. We identify leverage points where researchers can develop quality metrics and self-correcting documentation systems for agent-mediated use, and system designers can surface agent consumption patterns to documentation teams. Through this position paper, we call on the research community to investigate how agent-mediated documentation consumption reshapes documentation and its quality.